PBS0.ES: Parametric bootstrap for the efficient score statistic
In nshi-stat/netiim3: Improved Inference Methods for Network Meta-Analysis

Description Usage Arguments Details Value References Examples

Parametric bootstrap for the efficient score statistic to implement the improved tests developed by Noma et al. (2017).

1	PBS0.ES(y, S, ml0, mu0, B = 400)

`y`	N x p matrix of outcome variables.
`S`	Series of within-study covariance matrices of the outcome variables. A matrix or data frame with N rows and p(p+1)/2 columns.
`ml0`	A vector of (unconditional) maximum likelihood estimate of the random effects meta-analysis, computed by ML.
`mu0`	The value of the first component of the grand mean vector (corresponding to null hypothesis).
`B`	Number of resampling.

Please see Noma et al. (2017) for details.

Resampled B samples of the efficient score statistics.

Noma, H., Nagashima, K., Maruo, K., Gosho, M., Furukawa, T. A. (2017). Bartlett-type corrections and bootstrap adjustments of likelihood-based inference methods for network meta-analysis. ISM Research Memorandum 1205.

# dae <- data.aug.edit(smoking)
# y <- dae$y
# S <- dae$S

# beta1e <- 0.80

# C <- 0.95
# alpha <- 1 - C

# ml1 <- ML(y, S)

# a1 <- ml1$Coefficients[,1]
# a2 <- (ml1$`Between-studies_SD`)^2
# a3 <- a2*(ml1$`Between-studies_COR`)
# a4 <- c(a1, a2, a3)

# mu13 <- ml1$Coefficients[1, 1]
# ci3 <- ml1$Coefficients[1, 3:4]

# R <- qchisq(C, df = 1)

# beta1 <- log(beta1e)

# PBS2 <- PBS0.ES(y, S, ml0 = a4, mu0 = beta1, B = 400)

# ES0 <- ES(y, S, ml0 = a4, mu0 = beta1)
# ES1 <- ES0/mean(PBS2)

# ES0  # ordinary efficient score statistic
# ES1  # Bartlett-type adjusted efficient score statistic